1. Field of the Invention
The present invention relates to a moving object detection method and system, and more specifically to a technology of detecting a human hand based on depth information of stereoscopic vision.
2. Description of the Related Art
The detection of a moving object is one aspect of studies regarding a planar image process and a stereoscopic vision process. By detecting a different moving object, it is possible to implement a series of subsequent controls and operations, based on the detection of the different moving object. Specifically, for example, at present, the control of gestures based on stereoscopic vision is widely applied to various types of intelligent equipment, television sets, and game consoles. Gesture control is based on detection of a human hand. In the interaction process between a human and a computer, a human hand is typically continuously moving; however, there are cases where the hand stays at a certain position for a while. For example, an interaction system uses the staying time of the hand to trigger a single event. Therefore, it is necessary to detect a moving object such as a human hand, such that subsequent operations and interactions are accurately implemented.
In Chinese Patent Publication No. CN101437124A, the invention of which was invented by CHUNLIN LI and JIN WANG, disclosed on May 20, 2009, titled “Method for processing dynamic gesture identification signal facing (to) television set control”, a method for processing dynamic gesture identification signals for television set control is introduced. This method includes collecting user motion images in front of a television by using a camera built in the television, acquiring a moving object by analyzing the collected images, extracting information of a hand target from the moving object and generating a trajectory, and subsequently determining an instruction corresponding to the trajectory and generating a television control instruction. More specifically, this method includes constructing a background image by using an average value of a grayscale image of a continuous multi-frame, calculating the difference between the present frame and the background image and acquiring a plurality of objects, and analyzing the trajectories of these objects and determining the parts of the body to which these objects belong. When the trajectory in a certain area matches a feature that is defined in advance, it is determined that this area is the area where the hand is located. The feature defined in advance is constructed based on the assumption that the movement of the hand is more rapid than the movement of the head, and within a certain range of a distance between the hand and the head. U.S. Pat. No. 8,374,423B2, the invention of which was invented by Lee et. al., granted on Feb. 12, 2013, and titled “Motion Detection Using Depth Images”, introduces a moving object detection method based on a depth image, which includes setting an average value of depth images of a continuous multi-frame as a single reference image, obtaining the difference between the present depth image frame and the reference image in units of pixels and acquiring a moving image, and then determining the object to which the pixel belongs in the moving image. This method is for controlling an application by using position information of the moving object.
PCT Patent Publication No. WO2011045789, the invention of which was invented by Perski et. al., disclosed on Apr. 21, 2011, and titled “Computer Vision Gesture Based Control of a Device”, introduces a gesture control method. This method includes obtaining the difference between two frames of a 2D or 3D image and acquiring the difference image, detecting the edge in the image and acquiring an outline, and comparing the detected outline with a hand part outline model. This method requires that the user's hand is open and directly facing the camera, and the fingers of the hand are extended.
By a typical movement estimation method, it is not possible to accurately acquire the moving object that is the human hand. Furthermore, for example, when other parts of the body move (for example, the arm moves), the calculation of the moving object that is the human hand may not be sufficiently accurate. Therefore, there is a need for a technology to detect the moving object that is a human hand more accurately.